494c83cf7628afe24c4c0326bfd4c94b1614c34e
10 Commits
| Author | SHA1 | Message | Date | |
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 builtbykev
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494c83cf76 |
Hunt the window-bug class: three more paths, and the forward re-audit rule
in code PHASE 0 — getStatRows is the single base-rate path, so every branch is audited, plus the feature builders since l20_avg is the season reference projectionFor reads: getStatRows MLB -> estimator base fullLog CORRECT ( |
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builtbykev
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929fd81940 |
Repair the champion: it was reading ten games, not a season
PHASE 0 — the defect is real past the peek. Against a FAIR point-in-time baseline (each player's rate over games strictly before that date, >=10 prior games, box scores back to 05-01), the served champion LOSES on all four stats, three of four CIs excluding zero: hits 0.00251 vs 0.00774 CI [-0.0074,-0.0011] TB 0.00393 vs 0.00619 CI [-0.0055,-0.0003] rbi 0.02481 vs 0.03133 CI [-0.0153,-0.0005] runs 0.00181 vs 0.00683 CI [-0.0114,+0.0008] PHASE 1 — the cause is the WINDOW, not the weights. estimateProbability builds its base rate as the frequency over every row it is handed, and featureCache.getStatRows handed it res.last10. So the "season rate" was a TEN-GAME rate, and 0.4 of the forecast was the last five OF THOSE TEN. The 0.40 recency weight costs resolution on all four stats (-0.00086, -0.00107, -0.00562, -0.00365). Nudges are mixed and small -- harmful on hits and rbi, marginally helpful on TB and runs -- so they are left alone. PHASE 2 — two lines, no new data, no extra API call, because fullLog was already fetched by the same adapter call that produced last10: getStatRows now reads fullLog, and RECENCY_WEIGHT goes 0.40 -> 0.20. hits 0.00251 -> 0.00817 (tripled; now above the fair baseline) TB 0.00393 -> 0.00734 (above baseline; vs old CI [0.0020,0.0067]) rbi 0.02481 -> 0.02727 (still below baseline, CI includes zero) runs 0.00181 -> 0.00436 (still below baseline, CI includes zero) Gate stated exactly: hits and TB now exceed the fair baseline on the point estimate; rbi and runs remain below but EVERY CI now includes zero, so no stat reliably loses to a frequency table. That is a tie on rbi/runs, not a win, and it is reported as one. Only TB's improvement over the old champion is CI-confirmed; the rest are directional. STALE-FIT GATE: CALIBRATION_DEPLOYED is now EMPTY. The low-param maps were fitted on the retired forecast and fromLedger cannot rescue them -- settled ledger rows still carry OLD p_win, so refitting today would refit the retired forecast. Nothing is served calibrated until dates settle under the repaired champion, and the favourite-longshot bias must be re-measured rather than assumed to survive. The shadow duel is void. PHASE 3 — the hits factor lift is NOT re-measured, and cannot be yet: it needs settled rows produced BY the repaired champion, which ships in this commit. Replaying would score the factors against a reconstruction rather than the served forecast. Deferred, explicitly. The factors remain wired and transmitting; only their lift is unquantified on the new baseline. PHASE 4 — standing flag, and it is large: EVERY factor verdict in this programme, every null and every THEATER, was measured against a champion worse than a frequency table. Signal added to noise reads as noise. Prior verdicts may deserve re-audit. Logged, not re-run. Re-queued not built: rbi lineup-slot / RISP opportunity through the two-part gate, now landing on a repaired champion. Serving-path change by design; the byte-identical invariant inverted and all four stats move. Nine frozen model modules verified unchanged. No Bonferroni slot -- resolution accounting on the champion's own knobs. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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092f8f09cd |
Build opportunity_drift axis on challengerProjection (arch-v1)
Champion p_win and the live grade path are BYTE-IDENTICAL: the axis writes only to p_win_challenger / challenger_adjustments in the ledger. STEP 1 -- MAP THE INPUT. MLB_LOG_FIELD now maps at_bats -> 'atBats'. Deliberately NOT added to outcomeService's map or liveTracking's LIVE_BOX_FIELD: those exist to SETTLE and TRACK graded props, and nothing grades at-bats, so adding it there would imply a settlement path for a market we do not carry. A test asserts the settle map still lacks it. STEP 2 -- DRIFT, NOT LEVEL. opportunity_drift = mean(last-5 atBats) / (season atBats / games). The LEVEL is collinear with l20_avg (same games denominator; hits/game ~= (hits/AB) x (AB/game)), so the projection already embeds it multiplicatively and adding it would double-count. A deviation from the player's own baseline is the part the projection does not contain. HONEST ABSENCE throughout: fewer than 3 at-bat rows, no at-bats in the logs, or no season baseline all leave drift UNDEFINED -- never 1.0 by default and never 0. Number(null) === 0 here would read as "zero at-bats", the strongest possible fade, invented from missing data. Four tests cover the absent paths. STEP 3 -- THE AXIS. opportunityNudge composes in the same log-odds space as park and platoon (log of a ratio), with two guards the measured axes do not need: a +/-10% DEADBAND (a rest day or a blowout can move a 5-game window without any role change) and a tighter cap (0.15 vs the environment's 0.30) so a noisy PROXY cannot outvote measured signals. Every adjustment carries is_proxy: true and proxy_for: 'confirmed_batting_order' so nothing downstream can mistake it for a lineup feed. The axis can stand ALONE -- without it the early return would gate opportunity off on exactly the thin-classification rows it is most likely to help. Zero extra I/O: analyzeViaEngine1 attaches drift from the feature vector it has already built, and attachChallenger reads it off the grade. Nothing re-fetches in a loop that runs over hundreds of props. COLLINEARITY GUARD added to the coverage probe: Pearson r of drift against l20_avg / l5_avg / ab_per_game, returning null under n=8 rather than reporting a correlation on a handful of rows. If drift just re-encodes the projection, the axis is dead signal and gets shelved. Gates: 4,073 tests / 326 suites green; next build exit 0. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc |
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builtbykev
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63302d194e |
Wire MLB opp_rank_stat into live features (consumption path verified)
featureCache.teamFeatures now derives MLB opp_rank_stat from statsapi team
pitching splits when the ESPN path yields nothing — which for MLB is
always, because ESPN's MLB team endpoint carries no defensive metric at
all. mlbStatsAdapter.getTeamPitchingStats fetches all 30 teams in one free
unauthenticated call, cached at the season TTL.
CONSUMPTION PATH VERIFIED before wiring, not assumed:
featureCache.teamFeatures sets out.opp_rank_stat (line 338)
-> engine1.computeFactors READS features.opp_rank_stat (lines 96-102)
-> fires weak_opponent_defense (>=0.70) / top_opponent_defense (<=0.30)
So teamFeatures is the correct insertion point: the grader reads exactly
the field we populate. A value written anywhere else would have been a
dead end — computed, retained, and still not affecting the grade.
Contract preserved: the derived value goes into the SAME field with the
SAME 0-1 scale and the SAME high=weak polarity WNBA uses, so engine1 reads
one field with one meaning across sports. Isolated and best-effort — a
derivation failure leaves the field ABSENT (honest null), never a guessed
rank. Only fills when the ESPN path produced nothing, so WNBA behaviour is
untouched.
Suite 285/3435 green, build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
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builtbykev
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1a94ef5fcf |
Revive the dead probability layer + restore grade range ON MERIT
Folds re-sequenced steps 1+2 into one change (Kev's call): same bug
family — features wired to sources that return null.
THE PROBABILITY LAYER WAS DEAD IN PRODUCTION. p_win/ev_pct/kelly/
model_odds/value were absent on 0/8 live grades because
gameLogService.getGameLogs returns null for MLB by construction and
depends on the offline Python service for NBA/WNBA, so meta.gameLogs was
[] for every sport. This was the S46 bug in a second location — that fix
gave featureCache an MLB branch (why grades still worked) but never the
estimator. featureCache.getStatRows now supplies normalized rows
([{date,[statType]:v}], most-recent-first) for every sport, feeding the
estimator AND consistency AND game_count_in_7d from one fetch.
VERIFIED on real props: p_win 25/25 WNBA, 8/8 MLB (was 0).
GRADE RANGE, ON MERIT — never by rescaling (permanent founder ruling:
minting A's without new information is a relabelled B sold as an A and
corrupts an append-only ledger).
- refreshTeamStats wired into runSnapshot — it had ZERO production
callers, so opp_rank_stat was permanently null and a +/-1.0 factor
could never fire. Test-env no-op (opsNotify precedent).
- L20 made SYMMETRIC: both branches were delta +1.0, so the season
baseline could only ever ADD. No negative path was a structural reason
D was unreachable. New l20_contradicts_* carries -1.0.
- game_count_in_7d derived from real logged dates (heavy_workload_7d).
- NOT wired, deliberately, with reasons inline: teamId (no team_id
column; getFeatures reads it top-level; factor also needs a starter-id
list) and season_type (ESPN 2 = REGULAR season; threading it raw would
fire veteran_in_playoffs in July). Dead code dressed as a fix is the
thing we are removing, not adding.
CALIBRATION GUARD (found by verifying, not assuming): consistency CV is
NBA-tuned; for a Poisson-ish stat cv ~ 1/sqrt(mean), so any stat with
mean < 4 auto-classifies boom_bust. First verification run showed 8/8 MLB
props boom_bust — a blanket -1.0 that dropped the board to all-C. Floored
at CONSISTENCY_MIN_MEAN=4 -> 'unknown' below. Absent beats wrong. MLB
low-count stats therefore still get no consistency factor: honest, not
fixed. Scale-free index-of-dispersion classifier is the open follow-up.
CONFIDENCE IS NOT A PROBABILITY: payloads carry confidence_basis:
'grade_band'. Corrected mlb-grade-degradation.md — its "25/25
grade<->confidence agreement" is a TAUTOLOGY (confidence is derived FROM
the letter, so it would report 25/25 even if every grade were wrong), not
a validation. Removed dead mlbGrader.js (referenced only by its own test)
and the stale computeFeatures comment claiming a penalty that never ran.
VERIFICATION (scripts/verify-grade-range.js, real props/logs/engine):
WNBA 25 props B 68%->32%, C 32%->64%, D 0->1 (4%); 11-step spread went
from 2 steps to 5 (C/C+/B-/D). The D is earned: Angel Reese assists o2.5,
p_win 0.365. Nothing flooded — grades got HARDER. A did not emit locally
because opp_rank_stat needs the Redis cache only prod populates (local
ceiling +3.0 vs the +4.5 A needs); reachability is proven arithmetically
and locked in tests. Prod A-emission is the outstanding fingerprint.
MARKETING HOLD: "A-RATED" (AccuracyBadge, TopSignals) is unsupported
until that fingerprint. Confirmed honest fallbacks render today —
/api/ledger/accuracy has B and C buckets only, so the badge shows
"MODEL · 63% HIT" and TopSignals self-hides. Nothing fabricated ships.
Suite 276/3286 green, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
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builtbykev
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287c1c047a |
Wave 0: NBA/WNBA grade unlock — free ESPN per-athlete gamelog source
The Python nba_api service (gameLogService) is offline in prod, so
featureCache's non-MLB branch produced no l5/l20 averages →
projectionFor returned null → the ENTIRE NBA/WNBA slate refused
(insufficient_data). Only MLB actually graded.
Fix (free, no-auth, verified live):
- espnStatsAdapter.getPlayerGameLog(name, sport) — resolves name→ESPN
numeric athlete id via the v2 search (the v3 /search now returns
count:0; the v2 uid carries a:<id>, defaultLeagueSlug disambiguates
league), fetches the per-athlete gamelog, and parses per-game rows
keyed by VYNDR stat names (points/rebounds/assists/threes/steals/
blocks/turnovers + computed pra). Columns are indexed by the
response's own names[] array (NBA and WNBA orders DIFFER), never
positionally. Most-recent first, defensive (null on unrecognized
shape, never throws), cached (espngamelog:{sport}:{id} 4h + memory).
- featureCache.gameLogFeatures — falls back to the ESPN gamelog for
nba/wnba when the Python source returns null/empty, producing
l5/l10/l20 + rest_days + minutes_per_game via a new local
NBA_LOG_FIELD map + pure nbaGameLogFeatures (S11 three-map-split:
separate from MLB_LOG_FIELD).
Grade gates already whitelist all 8 NBA/WNBA stat types in both Node
paths (analyze.js + scan.js); no gate change needed.
Tests (hermetic, no network): espnGameLog.test.js (parser/resolver/
adapter) + featureCacheNba.test.js (the UNLOCK proof — empty features
refuse, ESPN-derived features grade). 3098 tests green; next build
exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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2ae8a5697e |
Session 56: Full audit — PropLine + boxscore + pipeline + sport coverage (2289 tests)
Research (verified against live MLB Stats / ESPN / The Odds APIs): - specs/propline-audit.md — every stat_type mapped against our 4-layer pipeline; real MLB boxscore fields; sport coverage status; pipeline gap analysis. - specs/vyndr-roadmap.md — priority-ordered Sessions 57–64 + coverage targets. - scripts/propline-audit.js + specs/audit-data/ (raw capture). Headline bug: oddsNormalizer mapped batter_rbis → 'rbis' while the whole grade/feature/outcome chain keys on 'rbi' — every PropLine RBI prop silently failed to grade AND settle. Fixed (+ regression test). Phase 4 — wired missing MLB stats end-to-end: - PropLine MLB markets 6 → 12 (+runs, walks, doubles, earned_runs, hits_allowed, outs — same request, no extra quota). - doubles/outs/triples added to featureCache + outcomeService MLB_LOG_FIELD and all three grade whitelists (analyze/scan/validation.py). Phase 6 — pipeline resilience: - opsNotify.js: ntfy alerts (never throws, test-disabled). Snapshot success/ stale/failure alerts; retry-once on hard odds error (not on empty slate). - Missed-cron watchdog (mostRecentExpectedSlot/isSnapshotOverdue); status probe now returns `overdue`. Coverage truth: MLB is the only end-to-end-live sport; outcome settlement is MLB-only (WNBA/NBA/soccer never settle) — documented as the #1 roadmap gap. Backend 2276 → 2289 tests (+13). Web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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78db55d499 |
Session 47: Name normalization + grade intel + ticker polish (2149 tests)
- Name normalization completed: NICKNAMES table (Matt↔Matthew, Mike↔Michael...)
resolved in nameKey, parenthetical team-tag strip "(STL)", verified accent-fold
(Iván/Ivan, José/Jose). Slate strip now DISPLAYS the normalized de-dotted name
("AJ Ewing" not "A.J. Ewing") via buildPlayerStripsFromProps.
- Complete MLB VYNDR INTELLIGENCE: mlbGameLogFeatures derives rest_days (days off
between latest games; 0=B2B) + ab_per_game (usage). buildIntelFields renders
usage as "X AB/G", rest as B2B/Xd, matchup from bvp_advantage fallback.
- Ticker SCAN dedup: pushTickerItems keeps one SCAN per sport (sport field or
text-prefix parse for legacy); MOVE/GRADE preserved; cap 50.
- BOMBER threshold prorated for mid-season (hr>=15 strong / >=10 mod) so June
sluggers classify BOMBER not FLEX/DRIVER.
Backend 2122 -> 2149 tests (+27), 179 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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c8fc9f577e |
Session 46: Grade card intel + name normalization + pitchers (2122 tests)
Three focused P1 fixes on the Session-45 snapshot model.
- Grade card intel ROOT CAUSE: gameLogService is NBA/WNBA-only (offline Python),
so MLB props never got l5_avg/l20_avg and buildIntelFields returned {}. Wired
MLB game logs into featureCache.gameLogFeatures via mlbStatsAdapter.getPlayerStats
(pure mlbGameLogFeatures + MLB stat_type->field map). buildIntelFields gained
playerStats/projection fallbacks for partial intel.
- Player name normalization: src/utils/playerName.js (+ web/src/lib copy):
normalizeName -> {display,key}. Strips periods, de-dots suffix, accent-folds
the key. Applied in snapshotService grouping, slateAdapter grade index +
player-strip merge (variants collapse, longest name shown), and
playerIntelService. "A.J. Ewing"/"AJ Ewing" + "Jazz Chisholm"/"Jr." now merge.
- MLB starting pitchers: new GET /api/schedule/:sport/pitchers (probablePitchers
service wrapping mlbStatsAdapter.getScheduleWithPitchers + best-effort ERA).
Slate fetches it, builds a team->pitcher map (full name + mascot match),
attaches pitchers to MLB GameCardData. + Next proxy.
Backend 2100 -> 2122 tests (+22), 176 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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1fa04dc776 | Sessions 5-7a: 955 tests, deployment ready |